Driving Assistance Device Using Skill-Based Data Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing driving skill improvement technologies provide ineffective advice when a driver's skill differs significantly from the sample driving skill, as comparisons between vastly different skill levels or driving types are not meaningful.
Innovation Solution
A driving assistance device that includes a driving data storage unit, skill classification unit, similarity calculation unit, difference detection unit, and assistance unit, which selects and compares driving data from highly skilled drivers of similar type to provide targeted improvement advice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driving advice is given based on comparison with highly skilled drivers, then the skill improvement potential is increased, but the advice becomes meaningless when the driver's skill level differs too much from the sample driver
Solution Approach 1:
The patent applies local quality by providing customized driving advice tailored to each driver's specific skill level and driving type. Instead of using a single universal standard, the system selects sample drivers who match the target driver's characteristics (driving type, skill level), ensuring the advice is locally optimized for that specific driver segment. This resolves the contradiction by making advice both precise (comparing with appropriately skilled drivers) and adaptable (matching driving types).
Solution Approach 2:
The system changes the parameters used for comparison by introducing multiple dimensions: driving type classification and skill level classification. Rather than comparing all drivers against a single high-skill standard, the system varies the comparison parameters to match the target driver's characteristics. This allows the system to select sample drivers with appropriate skill levels and driving types, resolving the contradiction between precision and adaptability.
2Adaptability or versatility
If driving advice is given based on generic sample data, then it can be applied to any driver, but the advice lacks effectiveness when the driver's driving type differs from the sample driver
Solution Approach 1:
The system implements local quality by segmenting drivers into different driving types (e.g., city driving, highway driving, sporty driving) and selecting sample drivers who match the target driver's type. This ensures that the advice is locally optimized for each driving type rather than using a generic one-size-fits-all approach, thereby improving relevance without sacrificing broad applicability across different driver segments.
Solution Approach 2:
The patent applies segmentation by dividing the driver population into distinct driving types based on driving behavior patterns. This segmentation allows the system to provide targeted advice for each type while maintaining a comprehensive coverage across all driver types. The segmentation resolves the contradiction by organizing sample data into meaningful groups that balance generality with specificity.
3Measurement precision
If driving skill classification is performed using complex analysis, then the accuracy of skill assessment is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system applies segmentation by dividing driving skill assessment into discrete skill levels (e.g., beginner, intermediate, advanced, expert) rather than attempting continuous precise measurement. This segmentation simplifies the processing complexity while maintaining sufficient accuracy for practical purposes. The classified skill levels are then used as key parameters for selecting appropriate sample drivers, resolving the contradiction between precision and complexity.
Solution Approach 2:
The system changes the assessment parameters from continuous complex metrics to discrete classified categories. By transforming continuous driving data into classified skill levels and driving types, the system reduces computational complexity while preserving the essential information needed for effective advice delivery. This parameter transformation resolves the contradiction by maintaining functional accuracy with reduced complexity.
Data Source
AI summary
A driving skill in input driving data is acquired from driving skill classification unit, driving data, which includes a driving skill higher than the driving skill in the input driving data and of which a similarity to the input driving data is at least a predetermined similarity, is selected from among driving data stored in a driving data storage unit, a difference between the selected driving data and the input driving data is detected, and a notice on the detected difference is issued as driving advice. As a result, it is possible to present suitable advice to improve a driving skill taking into account the skill and type of a driver.